{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When searching for networks using AMC, we need to be able to rank discovered-networks by their accuracy.  In reality, we use a multi-objective (such as accuracy and latency) for produce a reward signal to the agent, but we always need to know how well the discovered networks perform their classification task.  We don't need to know the \"true Top1\" accuracy of the models (the accuracy of the model trained to convergence): we just need to be able to rank them, in a stable manner.  For example, imagine we've discovered two networks in our search: netA and netB.   NetA gives us a low predicted-accuracy signal (\"search-Top1\"), and NetB gives us a high predicted-accuracy.  If NetA's true-Top1 is smaller than NetB's true-Top1, and this property holds for most pairs of discovered networks, then the ranking is considered stable.\n",
    "\n",
    "#### Question: \n",
    "* Does FM-reconstruction improve the stability of model ranking?\n",
    "\n",
    "#### Method:\n",
    "* Use an agent with a random policy, so the RL agent does not play a role. \n",
    "* Compare the ranking stability when using FM-reconstruction vs. when using fine-tuning.\n",
    "\n",
    "#### Baseline:\n",
    "* L1 ranking with Random agent\n",
    "* 1 FT epoch\n",
    "\n",
    "#### Test:\n",
    "* Reconstruction with Random agent\n",
    "* 1 FT epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib \n",
    "import csv\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interactive, interact, Layout\n",
    "import matplotlib.pylab as pylab\n",
    "import matplotlib.animation as animation\n",
    "from matplotlib import animation, rc\n",
    "from scipy.stats.stats import pearsonr\n",
    "from auto_compression_jupyter import *\n",
    "\n",
    "\n",
    "EXPERIMENTS_DIR = os.path.join(\"/experiments\", \"amc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**\n",
    "\n",
    "    time python3 ../../classifier_compression/multi-run.py ${AMC_EXP_PATH}/plain20-random-reconstruction_nondeterministic amc.py --arch=plain20_cifar ${CIFAR10_PATH} --resume=${CHECKPOINTS_PATH}/checkpoint.plain20_cifar.pth.tar --lr=0.05 --amc-protocol=mac-constrained --amc-action-range 0.05 1.0 --amc-target-density=0.5 -p=50 --etes=0.075 --amc-ft-epochs=0 --amc-prune-pattern=channels --amc-prune-method=fm-reconstruction --amc-agent-algo=Random-policy --amc-cfg=auto_compression_channels.yaml --evs=0.5 --etrs=0.5 --amc-rllib=random -j=1\n",
    "\n",
    "==> experiments/plain20-random-reconstruction/2019.07.22-120953/\n",
    "\n",
    "    time python parallel-finetune.py --scan-dir=${AMC_EXP_PATH}/plain20-random-reconstruction_nondeterministic/2019.07.23-124600 --arch=plain20_cifar --lr=0.005 --vs=0 -p=50 --epochs=60 --compress=../plain20_fine_tune.yaml ${CIFAR10_PATH} -j=1 --deterministic --epoch=1 --output-csv=ft_1epoch_results.csv\n",
    "    \n",
    "**Baseline**\n",
    "\n",
    "    time python3 ../../classifier_compression/multi-run.py ${AMC_EXP_PATH}/plain20-random-l1_rank amc.py --arch=plain20_cifar ${CIFAR10_PATH} --resume=${CHECKPOINTS_PATH}/checkpoint.plain20_cifar.pth.tar --lr=0.05 --amc-protocol=mac-constrained --amc-action-range 0.05 1.0 --amc-target-density=0.5 -p=50 --etes=0.075 --amc-ft-epochs=0 --amc-prune-pattern=channels --amc-prune-method=l1-rank --amc-agent-algo=Random-policy --amc-cfg=auto_compression_channels.yaml --evs=0.5 --etrs=0.5 --amc-rllib=random -j=1\n",
    "\n",
    "==> classifier_compression/experiments/plain20-random-l1_rank/2019.07.21-004045\n",
    "\n",
    "    time python parallel-finetune.py --scan-dir=${AMC_EXP_PATH}/plain20-random-l1_rank/2019.07.21-004045/ --arch=plain20_cifar --lr=0.005 --vs=0 -p=50 --epochs=60 --compress=../plain20_fine_tune.yaml ${CIFAR10_PATH} -j=1 --deterministic --epoch=1 --output-csv=ft_1epoch_results.csv\n",
    "\n",
    "==> classifier_compression/experiments/plain20-random-l1_rank/2019.07.21-004045/ft_1epoch_results.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "#### NOTE\n",
    "\n",
    "* We use ```multi-run.py``` to execute the experiment 4 times, and write the output of each experiment to a separate subdirectory of ```experiments/plain20-random-l1_rank/2019.07.21-004045```.\n",
    "* Then we use `parallel-finetune.py` to traverse these subdirectories.\n",
    "* In each experiement subdirectory there are checkpoints for each of the best discovered networks.  Each time we find a network that performs better than the best network discovered so far (as measured by our multi-objective), we save the checkpoint of that netowrk.\n",
    "* Script `parallel-finetune.py` will run a fine-tuning (re-training) session of each of the network-checkpoint files and write the accuracy and other metadata of each fine-tuned network to a CSV file.\n",
    "* So if we execute 4 experiments using `multi-run.py`, then the CSV file should have the results for checkpoints of each of these 4 experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_baseline = pd.read_csv(os.path.join(EXPERIMENTS_DIR,\n",
    "                                       \"plain20-random-l1_rank\",\n",
    "                                       \"2019.07.21-004045\",\n",
    "                                       \"ft_1epoch_results.csv\"))\n",
    "df_thesis = pd.read_csv(os.path.join(EXPERIMENTS_DIR,\n",
    "                                     \"plain20-random-reconstruction_nondeterministic\",\n",
    "                                     \"2019.07.23-124600\",\n",
    "                                     \"ft_1epoch_results.csv\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer:\n",
    "\n",
    "* When using L1-ranking (with no FT during the RL search process) with a Random agent, there is no corralation between the search Top1 accuracy used as reward, and the Top1 after 1 epoch of FT.\n",
    "* When using FM-reconstruction, search Top1 scores below ~15% show no corralation to the Top1 after 1 epoch of FT, but higher search Top1 scores show much better corralation (to be measured)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa147eb98d0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "plt.scatter(df_baseline.top1, df_baseline.search_top1, s=df_baseline.macs)\n",
    "plt.scatter(df_thesis.top1, df_thesis.search_top1, s=df_thesis.macs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, so clearly performing feature-map reconstruction helps create a much richer reward signal.<br>\n",
    "But how accurate is this signal?<br>\n",
    "We take some of the networks from the feature-map reconstruction experiment and fine-tune them for a longer period.\n",
    "\n",
    "\n",
    "#### Question:\n",
    "* What happens if we fine-tune the discovered networks for a longer time?<br>\n",
    "  NOTE: we fine-tune _AFTER_ the RL search is done, so this FT process does not help the agent.  And, in any case, we are using a random agent.\n",
    "  \n",
    "<code>\n",
    "time python parallel-finetune.py --scan-dir=${AMC_EXP_PATH}/plain20-random-reconstruction_nondeterministic/2019.07.23-124600 --arch=plain20_cifar --lr=0.005 --vs=0 -p=50 --epochs=60 --compress=../plain20_fine_tune.yaml ${CIFAR10_PATH} -j=1 --deterministic --epoch=20 --output-csv=ft_20epoch_results.csv\n",
    "</code>\n",
    "\n",
    "#### Answer:\n",
    "* The Pearson corralation **decreases** the more we fine-tune the solution.  This is not good...\n",
    "* But something interesting happens: the discovered networks that have search-Top1 below ~15% still have low ranking stability (i.e. they do not corrolate well with the \"true Top1\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best network: 82.54\n",
      "Pearson: 0.753\n",
      "Best network: 84.65\n",
      "Pearson: 0.698\n",
      "Best network: 85.97\n",
      "Pearson: 0.656\n",
      "Best network: 87.60\n",
      "Pearson: 0.594\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpath = os.path.join(EXPERIMENTS_DIR, \n",
    "                     \"plain20-random-reconstruction_nondeterministic\",\n",
    "                     \"2019.07.23-124600\")\n",
    "df_thesis3 = pd.read_csv(os.path.join(fpath, \"ft_3epoch_results.csv\"))\n",
    "df_thesis6 = pd.read_csv(os.path.join(fpath, \"ft_6epoch_results.csv\"))\n",
    "df_thesis20 = pd.read_csv(os.path.join(fpath, \"ft_20epoch_results.csv\"))\n",
    "\n",
    "def plot_networks2(df):\n",
    "    size = df.macs / max(df.macs) * 100\n",
    "    plt.scatter(df.top1, df.search_top1, s=size)\n",
    "    print(\"Best network: %.2f\" % max(df.top1))\n",
    "    df_sorted = df.sort_values(by=['top1'], inplace=False, ascending=False)\n",
    "\n",
    "\n",
    "create_fig(\"Discovered Networks:\\nComparing Search Top1 vs Fine-tuned Top1\\n\",\n",
    "           \"Fine-tuned Top1 Accuracy\",\n",
    "           \"Search Top1 Accuracy\")\n",
    "for df in (df_thesis, df_thesis3, df_thesis6, df_thesis20):\n",
    "    plot_networks2(df)\n",
    "    print(\"Pearson: %.3f\" % pearsonr(df.top1, df.search_top1)[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question (Part 2):\n",
    "What happens when we look at the discovered networks that have a Search Top1 > 20%? <br>\n",
    "I.e. if we remove the noisy low-grade networks, can we improve the corrallation?\n",
    "\n",
    "#### Answer (Part 2):\n",
    "Yes, the corralation increases and we have some evidence of a quality reward signal.  \n",
    "\n",
    "@todo: compare to the networks trained to **convergence**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson: 0.887\n",
      "Pearson: 0.905\n",
      "Pearson: 0.889\n",
      "Pearson: 0.901\n"
     ]
    }
   ],
   "source": [
    "create_fig(\"Discovered Networks:\\nComparing Search Top1 vs Fine-tuned Top1\\nsearch_top1 > 20%\",\n",
    "           \"Fine-tuned Top1 Accuracy\",\n",
    "           \"Search Top1 Accuracy\")\n",
    "for df in (df_thesis, df_thesis3, df_thesis6, df_thesis20):\n",
    "    df = df[df['search_top1'] > 20]\n",
    "    plot_networks2(df)\n",
    "    print(\"Pearson: %.3f\" % pearsonr(df.top1, df.search_top1)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question:\n",
    "\n",
    "* What happens if we fine-tune the networks we discovered using l1-ranking (without FM reconstruction)?\n",
    "* In other words, how many fine-tuning epochs do we need to perform during the RL search to get a cleaner, less noisy (i.e. more stable) search-Top1 (used in the multi-objective reward signal)?\n",
    "\n",
    "\n",
    "    time python parallel-finetune.py --scan-dir=${AMC_EXP_PATH}/plain20-random-l1_rank/2019.07.21-004045/ --arch=plain20_cifar --lr=0.005 --vs=0 -p=50 --epochs=60 --compress=../plain20_fine_tune.yaml ${CIFAR10_PATH} -j=1 --deterministic --epoch=3 --output-csv=ft_3epoch_results.csv\n",
    "\n",
    "    time python parallel-finetune.py --scan-dir=${AMC_EXP_PATH}/plain20-random-l1_rank/2019.07.21-004045/ --arch=plain20_cifar --lr=0.005 --vs=0 -p=50 --epochs=60 --compress=../plain20_fine_tune.yaml ${CIFAR10_PATH} -j=1 --deterministic --epoch=6 --output-csv=ft_6epoch_results.csv\n",
    "\n",
    "    time python parallel-finetune.py --scan-dir=${AMC_EXP_PATH}/plain20-random-l1_rank/2019.07.21-004045/ --arch=plain20_cifar --lr=0.005 --vs=0 -p=50 --epochs=60 --compress=../plain20_fine_tune.yaml ${CIFAR10_PATH} -j=1 --deterministic --epoch=20 --output-csv=ft_20epoch_results.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer:\n",
    "\n",
    "* Fine-tuning longer does not help increase the quality of the search-Top1 accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best network: 72.17\n",
      "Pearson: 0.129\n",
      "Best network: 81.88\n",
      "Pearson: 0.137\n",
      "Best network: 85.47\n",
      "Pearson: 0.073\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpath = os.path.join(EXPERIMENTS_DIR, \n",
    "                     \"plain20-random-l1_rank\",\n",
    "                     \"2019.07.21-004045\")\n",
    "\n",
    "df_baseline6 = pd.read_csv(os.path.join(fpath, \"ft_6epoch_results.csv\"))\n",
    "df_baseline20 = pd.read_csv(os.path.join(fpath, \"ft_20epoch_results.csv\"))\n",
    "#df_baseline60 = pd.read_csv(os.path.join(fpath, \"ft_60epoch_results.csv\"))\n",
    "\n",
    "    \n",
    "create_fig(\"Discovered Networks:\\nComparing Search Top1 vs Fine-tuned Top1\\n\",\n",
    "           xlabel=\"Fine-tuned Top1 Accuracy\",\n",
    "           ylabel=\"Search Top1 Accuracy\")\n",
    "for df in (df_baseline, df_baseline6, df_baseline20): #, df_baseline60):\n",
    "    plot_networks2(df)\n",
    "    print(\"Pearson: %.3f\" % pearsonr(df.top1, df.search_top1)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What happens when we randomly sample Resnet20 (instead of Plain20) networks?\n",
    "\n",
    "<code>\n",
    "time python3 ../../classifier_compression/multi-run.py ${AMC_EXP_PATH}/resnet20-random-reconstruction_nondeterministic amc.py --arch=resnet20_cifar ${CIFAR10_PATH} --resume=../../ssl/checkpoints/checkpoint_trained_dense.pth.tar --lr=0.05 --amc-protocol=mac-constrained --amc-action-range 0.05 1.0 --amc-target-density=0.5 -p=50 --etes=0.075 --amc-ft-epochs=0 --amc-prune-pattern=channels --amc-prune-method=fm-reconstruction --amc-agent-algo=Random-policy --amc-cfg=auto_compression_channels.yaml --evs=0.5 --etrs=0.5 --amc-rllib=random -j=1\n",
    "</code>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    time python ../../classifier_compression/parallel-finetune.py --scan-dir=${AMC_EXP_PATH}/resnet20-random-reconstruction_nondeterministic/2019.07.31-213149 --arch=resnet20_cifar --lr=0.1 --vs=0 -p=50 --epochs=60 --compress=../plain20_fine_tune.yaml ${CIFAR10_PATH} -j=1 --deterministic --output-csv=ft_60epoch_results.csv --processes=16"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see so many high-scoring networks that we discover randomly!<br>\n",
    "A (uniform) random policy performs surpisingly well, but the Pearson corralation is not very high. This means that we'll need to sample and fine-tune quite a few of the high-performing search-top1 networks, to find the best performing converged network.\n",
    "\n",
    "We can also [graph the filter-pruning sensitivity](https://github.com/IntelLabs/distiller/blob/master/jupyter/sensitivity_analysis.ipynb) of ResNet20 vs. Plain20 vs. ResNet50 and see that of the three models, Plain20 is most sensitive to filter pruning, while the other two are quite robust."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best network: 90.98\n",
      "Pearson: 0.755\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpath = os.path.join(EXPERIMENTS_DIR, \n",
    "                     \"resnet20-random-reconstruction_nondeterministic\",\n",
    "                     \"2019.07.31-213149\")\n",
    "\n",
    "df_baseline60 = pd.read_csv(os.path.join(fpath, \"ft_60epoch_results.csv\"))\n",
    "\n",
    "create_fig(\"Discovered Networks:\\nComparing Search Top1 vs Fine-tuned Top1\\n\",\n",
    "           xlabel=\"Fine-tuned Top1 Accuracy\",\n",
    "           ylabel=\"Search Top1 Accuracy\")\n",
    "plot_networks2(df_baseline60)\n",
    "print(\"Pearson: %.3f\" % pearsonr(df_baseline60.top1, df_baseline60.search_top1)[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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